In addition, to stabilize the learning, we enforce our networks to estimate the residual matching scores as complementary details. We add residual connection in order to expedite the learning process [1]}, [2]}, [3]}, accounting for fact that at the initial phase when the input \(\mathcal {M}_p\)  is fed, erroneous matching scores are inferred due to randomly-initialized parameters of transformers, which could complicate the learning process as the networks need to learn the complete matching details from random matching scores.
